# Data Analyst

**Company:** [Sardine](https://hotfix.jobs/companies/sardine)
**Location:** Remote
**Role:** Data Analytics
**Salary:** $115k – $175k/yr
**Experience:** 3+ years
**Skills:** SQL, Python, R, A/B Testing, Lift Analysis, Precision/Recall, Auc/Roc, Data Quality Assessment, Vendor Data Evaluation, Fraud Detection
**Posted:** 2026-07-01

> Data Analyst on the Fraud Intelligence team responsible for evaluating third-party vendor data signals through lift analyses, backtests, and statistical frameworks to improve fraud detection outcomes. Requires 3-5 years analytical experience, strong SQL and Python/R skills, and expertise in metrics like precision/recall and A/B testing, ideally in fintech or fraud.

## Job Description

## Responsibilities
- Design and execute structured evaluation frameworks to assess the quality, coverage, and fraud-signal value of incoming data assets from vendor partners.
- Build lift analyses, backtests, and champion/challenger comparisons to quantify the incremental value of new data signals against our existing fraud detection stack.
- Profile vendor datasets for completeness, freshness, match rates, and population coverage across verticals (crypto, fintech, neobanks, e-commerce, etc.).
- Collaborate with fraud leadership to define evaluation criteria tied to real fraud outcomes — false positive rates, catch rates, precision/recall tradeoffs.
- Translate vendor data findings into clear, actionable recommendations: adopt, pilot, deprioritize, or decline.
- Partner with data engineering to define ingestion requirements and ensure test environments reflect production-like conditions.
- Document evaluation results and maintain an internal knowledge base on vendor data performance over time.
- Support ad hoc deep dives into fraud trends, model performance, and client-specific data questions as needed.

## Requirements
- 3–5 years of experience in data analysis, data science, or a related analytical role — ideally in fraud, risk, fintech, or a data-heavy B2B SaaS environment.
- Proficiency in SQL (required) and Python or R for data manipulation, statistical analysis, and visualization.
- Solid understanding of evaluation metrics and statistical concepts: precision/recall, AUC/ROC, lift, population distributions, and A/B testing basics.
- Experience working with external or third-party datasets — assessing data quality, match rates, and signal value.
- Strong written and verbal communication skills; ability to synthesize complex analysis into clear narratives for non-technical stakeholders.
- Comfort with ambiguity and the ability to define your own structure in a fast-moving environment.

## Nice-to-Haves
- Familiarity with fraud signals and data types: device fingerprinting, identity graph data, consortium data, behavioral signals, email/phone intelligence.
- Experience in a vendor evaluation, data partnerships, or procurement-adjacent analytical role.
- Exposure to machine learning concepts and feature engineering, even if not in a full ML engineering capacity.
- Experience working across fintech verticals such as crypto, BNPL, neobanks, or payments.

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